Overview

Dataset statistics

Number of variables9
Number of observations62
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.0 KiB
Average record size in memory82.1 B

Variable types

Numeric8
DateTime1

Dataset

Description충청남도 서산시에 등록된 자동차에 대한 등록현행자료로 년별, 월별, 승용차, 화물차, 특수차, 이륜자동차 등록 통계를 낸 자료
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=451&beforeMenuCd=DOM_000000201001001000&publicdatapk=15000832

Alerts

년도 is highly overall correlated with 합계 and 5 other fieldsHigh correlation
합계 is highly overall correlated with 년도 and 5 other fieldsHigh correlation
승용차 is highly overall correlated with 년도 and 5 other fieldsHigh correlation
승합차 is highly overall correlated with 년도 and 5 other fieldsHigh correlation
화물차 is highly overall correlated with 년도 and 5 other fieldsHigh correlation
특수차 is highly overall correlated with 년도 and 5 other fieldsHigh correlation
이륜자동차 is highly overall correlated with 년도 and 5 other fieldsHigh correlation
합계 has unique valuesUnique
승용차 has unique valuesUnique
기준일 has unique valuesUnique

Reproduction

Analysis started2024-01-09 22:34:07.422912
Analysis finished2024-01-09 22:34:12.785460
Duration5.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.0968
Minimum2011
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-01-10T07:34:12.827354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2011
Q12012
median2013
Q32014
95-th percentile2015
Maximum2016
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5009252
Coefficient of variation (CV)0.00074558023
Kurtosis-1.1687147
Mean2013.0968
Median Absolute Deviation (MAD)1
Skewness0.070379128
Sum124812
Variance2.2527763
MonotonicityDecreasing
2024-01-10T07:34:12.924197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2015 12
19.4%
2014 12
19.4%
2013 12
19.4%
2012 12
19.4%
2011 12
19.4%
2016 2
 
3.2%
ValueCountFrequency (%)
2011 12
19.4%
2012 12
19.4%
2013 12
19.4%
2014 12
19.4%
2015 12
19.4%
2016 2
 
3.2%
ValueCountFrequency (%)
2016 2
 
3.2%
2015 12
19.4%
2014 12
19.4%
2013 12
19.4%
2012 12
19.4%
2011 12
19.4%

월
Real number (ℝ)

Distinct12
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3387097
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-01-10T07:34:13.017884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5387483
Coefficient of variation (CV)0.55827581
Kurtosis-1.2534075
Mean6.3387097
Median Absolute Deviation (MAD)3
Skewness0.042959772
Sum393
Variance12.522739
MonotonicityNot monotonic
2024-01-10T07:34:13.113999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 6
9.7%
1 6
9.7%
12 5
8.1%
11 5
8.1%
10 5
8.1%
9 5
8.1%
8 5
8.1%
7 5
8.1%
6 5
8.1%
5 5
8.1%
Other values (2) 10
16.1%
ValueCountFrequency (%)
1 6
9.7%
2 6
9.7%
3 5
8.1%
4 5
8.1%
5 5
8.1%
6 5
8.1%
7 5
8.1%
8 5
8.1%
9 5
8.1%
10 5
8.1%
ValueCountFrequency (%)
12 5
8.1%
11 5
8.1%
10 5
8.1%
9 5
8.1%
8 5
8.1%
7 5
8.1%
6 5
8.1%
5 5
8.1%
4 5
8.1%
3 5
8.1%

합계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83898.694
Minimum75205
Maximum93921
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-01-10T07:34:13.231825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum75205
5-th percentile76124.45
Q178465.5
median83959
Q388356.5
95-th percentile92851.4
Maximum93921
Range18716
Interquartile range (IQR)9891

Descriptive statistics

Standard deviation5599.9105
Coefficient of variation (CV)0.066746099
Kurtosis-1.1826675
Mean83898.694
Median Absolute Deviation (MAD)4911.5
Skewness0.12021319
Sum5201719
Variance31358998
MonotonicityNot monotonic
2024-01-10T07:34:13.356114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93921 1
 
1.6%
78362 1
 
1.6%
82938 1
 
1.6%
82556 1
 
1.6%
82275 1
 
1.6%
82094 1
 
1.6%
81697 1
 
1.6%
81524 1
 
1.6%
76209 1
 
1.6%
81011 1
 
1.6%
Other values (52) 52
83.9%
ValueCountFrequency (%)
75205 1
1.6%
75427 1
1.6%
75765 1
1.6%
76120 1
1.6%
76209 1
1.6%
76381 1
1.6%
76618 1
1.6%
76861 1
1.6%
77005 1
1.6%
77484 1
1.6%
ValueCountFrequency (%)
93921 1
1.6%
93619 1
1.6%
93266 1
1.6%
92872 1
1.6%
92460 1
1.6%
92267 1
1.6%
92005 1
1.6%
91436 1
1.6%
90932 1
1.6%
90559 1
1.6%

승용차
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52096.597
Minimum46054
Maximum59931
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-01-10T07:34:13.480746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum46054
5-th percentile46745.4
Q148581.75
median51605
Q355325.25
95-th percentile58946.65
Maximum59931
Range13877
Interquartile range (IQR)6743.5

Descriptive statistics

Standard deviation4028.4938
Coefficient of variation (CV)0.077327388
Kurtosis-1.0739633
Mean52096.597
Median Absolute Deviation (MAD)3354
Skewness0.35252128
Sum3229989
Variance16228762
MonotonicityNot monotonic
2024-01-10T07:34:13.595748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59931 1
 
1.6%
48435 1
 
1.6%
50832 1
 
1.6%
50530 1
 
1.6%
50300 1
 
1.6%
50154 1
 
1.6%
49845 1
 
1.6%
49710 1
 
1.6%
49462 1
 
1.6%
49283 1
 
1.6%
Other values (52) 52
83.9%
ValueCountFrequency (%)
46054 1
1.6%
46194 1
1.6%
46437 1
1.6%
46736 1
1.6%
46924 1
1.6%
47086 1
1.6%
47322 1
1.6%
47448 1
1.6%
47875 1
1.6%
47980 1
1.6%
ValueCountFrequency (%)
59931 1
1.6%
59688 1
1.6%
59331 1
1.6%
58965 1
1.6%
58598 1
1.6%
58431 1
1.6%
58179 1
1.6%
57719 1
1.6%
57326 1
1.6%
57045 1
1.6%

승합차
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3686.7903
Minimum3378
Maximum3840
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-01-10T07:34:13.716364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3378
5-th percentile3602.3
Q13642.5
median3682.5
Q33730.25
95-th percentile3836.7
Maximum3840
Range462
Interquartile range (IQR)87.75

Descriptive statistics

Standard deviation86.60957
Coefficient of variation (CV)0.023491862
Kurtosis3.047168
Mean3686.7903
Median Absolute Deviation (MAD)44.5
Skewness-0.80167898
Sum228581
Variance7501.2176
MonotonicityNot monotonic
2024-01-10T07:34:13.831659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3663 2
 
3.2%
3622 2
 
3.2%
3837 2
 
3.2%
3831 2
 
3.2%
3613 2
 
3.2%
3685 1
 
1.6%
3711 1
 
1.6%
3378 1
 
1.6%
3715 1
 
1.6%
3692 1
 
1.6%
Other values (47) 47
75.8%
ValueCountFrequency (%)
3378 1
1.6%
3406 1
1.6%
3596 1
1.6%
3602 1
1.6%
3608 1
1.6%
3609 1
1.6%
3610 1
1.6%
3613 2
3.2%
3617 1
1.6%
3620 1
1.6%
ValueCountFrequency (%)
3840 1
1.6%
3838 1
1.6%
3837 2
3.2%
3831 2
3.2%
3809 1
1.6%
3793 1
1.6%
3792 1
1.6%
3780 1
1.6%
3766 1
1.6%
3743 1
1.6%

화물차
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16755.242
Minimum11307
Maximum18154
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-01-10T07:34:13.957774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11307
5-th percentile15930.25
Q116226.25
median16719
Q317352.25
95-th percentile18041.05
Maximum18154
Range6847
Interquartile range (IQR)1126

Descriptive statistics

Standard deviation982.84636
Coefficient of variation (CV)0.058659037
Kurtosis14.505202
Mean16755.242
Median Absolute Deviation (MAD)546.5
Skewness-2.6079789
Sum1038825
Variance965986.97
MonotonicityNot monotonic
2024-01-10T07:34:14.105666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16167 2
 
3.2%
18154 1
 
1.6%
16225 1
 
1.6%
16594 1
 
1.6%
16551 1
 
1.6%
16503 1
 
1.6%
16479 1
 
1.6%
16459 1
 
1.6%
16378 1
 
1.6%
16336 1
 
1.6%
Other values (51) 51
82.3%
ValueCountFrequency (%)
11307 1
1.6%
15791 1
1.6%
15864 1
1.6%
15929 1
1.6%
15954 1
1.6%
16021 1
1.6%
16039 1
1.6%
16064 1
1.6%
16083 1
1.6%
16130 1
1.6%
ValueCountFrequency (%)
18154 1
1.6%
18090 1
1.6%
18074 1
1.6%
18044 1
1.6%
17985 1
1.6%
17956 1
1.6%
17924 1
1.6%
17863 1
1.6%
17825 1
1.6%
17731 1
1.6%

특수차
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)77.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean317.25806
Minimum252
Maximum416
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-01-10T07:34:14.253758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum252
5-th percentile262.05
Q1269
median312.5
Q3354
95-th percentile412
Maximum416
Range164
Interquartile range (IQR)85

Descriptive statistics

Standard deviation52.683383
Coefficient of variation (CV)0.16605845
Kurtosis-0.92400261
Mean317.25806
Median Absolute Deviation (MAD)43.5
Skewness0.62813823
Sum19670
Variance2775.5389
MonotonicityNot monotonic
2024-01-10T07:34:14.386305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
265 5
 
8.1%
272 3
 
4.8%
412 3
 
4.8%
281 2
 
3.2%
322 2
 
3.2%
267 2
 
3.2%
268 2
 
3.2%
269 2
 
3.2%
270 2
 
3.2%
261 1
 
1.6%
Other values (38) 38
61.3%
ValueCountFrequency (%)
252 1
 
1.6%
259 1
 
1.6%
261 1
 
1.6%
262 1
 
1.6%
263 1
 
1.6%
264 1
 
1.6%
265 5
8.1%
267 2
 
3.2%
268 2
 
3.2%
269 2
 
3.2%
ValueCountFrequency (%)
416 1
 
1.6%
415 1
 
1.6%
413 1
 
1.6%
412 3
4.8%
407 1
 
1.6%
405 1
 
1.6%
403 1
 
1.6%
393 1
 
1.6%
385 1
 
1.6%
383 1
 
1.6%

이륜자동차
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11042.806
Minimum9271
Maximum11837
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-01-10T07:34:14.500143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9271
5-th percentile9320.5
Q19915
median11585
Q311714
95-th percentile11822.9
Maximum11837
Range2566
Interquartile range (IQR)1799

Descriptive statistics

Standard deviation994.55427
Coefficient of variation (CV)0.090063542
Kurtosis-0.86762268
Mean11042.806
Median Absolute Deviation (MAD)166
Skewness-1.0233883
Sum684654
Variance989138.19
MonotonicityNot monotonic
2024-01-10T07:34:14.616777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11639 3
 
4.8%
11821 2
 
3.2%
9397 2
 
3.2%
9409 2
 
3.2%
9863 1
 
1.6%
11560 1
 
1.6%
11540 1
 
1.6%
11532 1
 
1.6%
11537 1
 
1.6%
11541 1
 
1.6%
Other values (47) 47
75.8%
ValueCountFrequency (%)
9271 1
1.6%
9277 1
1.6%
9296 1
1.6%
9320 1
1.6%
9330 1
1.6%
9397 2
3.2%
9402 1
1.6%
9406 1
1.6%
9409 2
3.2%
9410 1
1.6%
ValueCountFrequency (%)
11837 1
1.6%
11827 1
1.6%
11824 1
1.6%
11823 1
1.6%
11821 2
3.2%
11818 1
1.6%
11790 1
1.6%
11768 1
1.6%
11763 1
1.6%
11758 1
1.6%

기준일
Date

UNIQUE 

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size628.0 B
Minimum2011-01-31 00:00:00
Maximum2016-02-29 00:00:00
2024-01-10T07:34:14.724488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:14.877071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-01-10T07:34:11.965154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:07.633356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:08.180414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:08.733051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:09.341526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:09.928131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:10.474657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:11.367267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:12.047168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:07.695877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:08.249389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:08.813241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:09.414524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:09.991613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:10.545575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:11.436144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:12.118997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:07.759424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:08.312276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:08.881959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:09.488276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:10.054639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:10.635386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:11.505837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:12.196713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:07.828230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:08.385351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:08.957033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:09.563076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:10.131149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:10.732105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:11.584609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:12.287263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:07.903313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:08.455511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:09.040292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:09.637951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:10.205098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:10.840200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:11.663025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:12.367386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:07.967567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:08.523413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:09.109001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:09.707134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:10.268775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:10.917424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:11.733294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:12.453373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:08.042450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:08.598919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:09.190239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:09.788693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:10.344166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:11.220534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:11.815331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:12.522767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:08.113608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:08.669820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:09.268246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:09.860697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:10.413495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:11.289857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:11.886946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:34:14.992217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도월합계승용차승합차화물차특수차이륜자동차기준일
년도1.0000.0000.9590.9150.8570.8520.9630.5181.000
월0.0001.0000.0000.0000.0000.0000.0000.0001.000
합계0.9590.0001.0000.9680.7880.9820.9700.7501.000
승용차0.9150.0000.9681.0000.7940.9700.9400.7331.000
승합차0.8570.0000.7880.7941.0000.8310.6390.6161.000
화물차0.8520.0000.9820.9700.8311.0000.8860.7241.000
특수차0.9630.0000.9700.9400.6390.8861.0000.4811.000
이륜자동차0.5180.0000.7500.7330.6160.7240.4811.0001.000
기준일1.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-01-10T07:34:15.336540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도월합계승용차승합차화물차특수차이륜자동차
년도1.000-0.0800.9740.980-0.8210.9740.9370.972
월-0.0801.0000.0970.1110.0240.0820.1100.127
합계0.9740.0971.0000.992-0.7990.9980.9570.985
승용차0.9800.1110.9921.000-0.8140.9870.9550.993
승합차-0.8210.024-0.799-0.8141.000-0.802-0.720-0.807
화물차0.9740.0820.9980.987-0.8021.0000.9550.980
특수차0.9370.1100.9570.955-0.7200.9551.0000.956
이륜자동차0.9720.1270.9850.993-0.8070.9800.9561.000

Missing values

2024-01-10T07:34:12.621570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:34:12.743571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

년도월합계승용차승합차화물차특수차이륜자동차기준일
0201629392159931359618154416118242016-02-29
1201619361959688360818090412118212016-01-31
22015129326659331362218074412118272015-12-31
32015119287258965363218044413118182015-11-30
42015109246058598364217985412118232015-10-31
5201599226758431364417956415118212015-09-30
6201589200558179365817924407118372015-08-31
7201579143657719365917863405117902015-07-31
8201569093257326362017825403117582015-06-30
9201559055957045362217731393117682015-05-31
년도월합계승용차승합차화물차특수차이륜자동차기준일
52201110776224798037801618327794022011-10-31
5320119774844787537931613027294142011-09-30
5420118770054744837921608327294102011-08-31
5520117768614732238091606426993972011-07-31
5620116766184708638311603926593972011-06-30
5720115763814692438371602126993302011-05-31
5820114761204673638401595427093202011-04-30
5920113757654643738381592926592962011-03-31
6020112754274619438311586426192772011-02-28
6120111752054605438371579125292712011-01-31